Image segmentation is a process of dividing an image into regions of interest, and includes discrimination of objects in an image from a background. Thresholding is one technique for segmentation. In bilevel thresholding, each image pixel is assigned to one of two classes according to whether its intensity (gray level or color) is greater or less than a specified threshold, resulting in a binary image. In multilevel thresholding, the entire image is thresholded multiple times, each time with a different constant threshold, resulting in multiple binary images.
One common application is optical character recognition (OCR), where image pixels are typically segmented into characters by thresholding. Consider, for example, an image of black text on a white background. A histogram of all the intensity values in the image will have two dominant peaks: one peak corresponding to the intensity value of the black text, and a second peak corresponding to the intensity value of the white background. If a threshold is set at an intensity value that is in the bottom of the valley between the two peaks, then any pixel having an intensity value darker than the threshold may be assigned to text, and any pixel having a intensity value lighter than the threshold may be assigned to background.
In the case of black text on a white background, a constant global threshold may be determined from a intensity value histogram of the entire image. However, many images of interest are more complex than just black text against a white background. For example, an image may include blocks of color (that is, the background may vary), text may overlap blocks of different colors, and text may be lighter than the local background. For complex images, the threshold may be dynamic, varying depending on the location of the pixel of interest within the image. A dynamic threshold may be dependent on intensity value data over a region of an image, or a dynamic threshold may vary from pixel to pixel. See, for example, Joan S. Weszka, “A Survey of Threshold Selection Techniques”, Computer Vision, Graphics, and Image Processing 7, 259–265 (1978) and Sahoo et al., “A Survey of Thresholding Techniques”, Computer Vision, Graphics, and Image Processing 41, 233–260 (1988).
Particular problems for thresholding include determination of a suitable threshold at the boundaries of objects, determination of a suitable threshold for thin objects (where there are few object intensity values in the histogram), and determination of a suitable threshold when there are areas of interest that are lighter than the background.
There is a need for improved segmentation of complex images using bilevel thresholding.